1. Field
This disclosure is generally related to techniques for electronic prognostication for computer systems. More specifically, this disclosure is related to a method and an apparatus that generates a training data set for a pattern-recognition model for electronic prognostication for a computer system.
2. Related Art
Typically, in order to generate a pattern-recognition model for electronic prognostication for a computer system, the computer system undergoes a training phase during which one or more performance parameters from the computer system are monitored and the pattern-recognition model is trained. Usually, the training period lasts long enough to capture performance parameters from the full range of normal operation of the computer system. For example, a computer system may often have to be monitored not only on busy workdays, but also on weekends, during lulls in usage, and during routine idiosyncrasies such as network backups. As a result, a computer system may have to be monitored for 10 days or more to train a pattern-recognition model to accurately capture the full range of operation which the computer system may experience during its service life. However, during this long training period the pattern-recognition model is not used for electronic prognostication, leaving the computer system vulnerable to failure without warning. Additionally, long training periods can delay the deployment of new computer systems.
Hence, what is needed is a method and system that trains a pattern-recognition model for electronic prognostication for a computer system without the above-described problems.